Zobrazeno 1 - 10
of 133
pro vyhledávání: '"Burt, David R"'
Wildfire frequency is increasing as the climate changes, and the resulting air pollution poses health risks. Just as people routinely use weather forecasts to plan their activities around precipitation, reliable air quality forecasts could help indiv
Externí odkaz:
http://arxiv.org/abs/2409.05866
A data analyst might worry about generalization if dropping a very small fraction of data points from a study could change its substantive conclusions. Finding the worst-case data subset to drop poses a combinatorial optimization problem. To overcome
Externí odkaz:
http://arxiv.org/abs/2408.09008
Spatial prediction tasks are key to weather forecasting, studying air pollution, and other scientific endeavors. Determining how much to trust predictions made by statistical or physical methods is essential for the credibility of scientific conclusi
Externí odkaz:
http://arxiv.org/abs/2402.03527
Autor:
Berlinghieri, Renato, Trippe, Brian L., Burt, David R., Giordano, Ryan, Srinivasan, Kaushik, Özgökmen, Tamay, Xia, Junfei, Broderick, Tamara
Publikováno v:
Proceedings of the 40th International Conference on Machine Learning, PMLR 202:2113-2163, 2023
Given sparse observations of buoy velocities, oceanographers are interested in reconstructing ocean currents away from the buoys and identifying divergences in a current vector field. As a first and modular step, we focus on the time-stationary case
Externí odkaz:
http://arxiv.org/abs/2302.10364
Publikováno v:
Advances in Neural Information Processing Systems (New Orleans), 2022
The kernel function and its hyperparameters are the central model selection choice in a Gaussian proces (Rasmussen and Williams, 2006). Typically, the hyperparameters of the kernel are chosen by maximising the marginal likelihood, an approach known a
Externí odkaz:
http://arxiv.org/abs/2211.02476
Autor:
Terenin, Alexander, Burt, David R., Artemev, Artem, Flaxman, Seth, van der Wilk, Mark, Rasmussen, Carl Edward, Ge, Hong
Publikováno v:
Journal of Machine Learning Research, 2024
Gaussian processes are frequently deployed as part of larger machine learning and decision-making systems, for instance in geospatial modeling, Bayesian optimization, or in latent Gaussian models. Within a system, the Gaussian process model needs to
Externí odkaz:
http://arxiv.org/abs/2210.07893
The Chernoff bound is a well-known tool for obtaining a high probability bound on the expectation of a Bernoulli random variable in terms of its sample average. This bound is commonly used in statistical learning theory to upper bound the generalisat
Externí odkaz:
http://arxiv.org/abs/2205.07880
Bayesian neural networks (BNNs) combine the expressive power of deep learning with the advantages of Bayesian formalism. In recent years, the analysis of wide, deep BNNs has provided theoretical insight into their priors and posteriors. However, we h
Externí odkaz:
http://arxiv.org/abs/2202.11670
Recent work in scalable approximate Gaussian process regression has discussed a bias-variance-computation trade-off when estimating the log marginal likelihood. We suggest a method that adaptively selects the amount of computation to use when estimat
Externí odkaz:
http://arxiv.org/abs/2109.09417
In this paper, we investigate the question: Given a small number of datapoints, for example N = 30, how tight can PAC-Bayes and test set bounds be made? For such small datasets, test set bounds adversely affect generalisation performance by withholdi
Externí odkaz:
http://arxiv.org/abs/2106.03542